@inproceedings{lv-etal-2026-specsteer,
title = "{S}pec{S}teer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation",
author = "Lv, Hang and
Liang, Sheng and
Wang, Hao and
Zhang, Yongyue and
Gu, Hongchao and
Guo, Wei and
Lian, Defu and
Liu, Yong and
Chen, Enhong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.47/",
pages = "1029--1046",
ISBN = "979-8-89176-459-0",
abstract = "Realizing personalized intelligence faces a core dilemma: sending user history to centralized large language models raises privacy concerns, while on-device small language models lack the reasoning capacity required for high-quality generation. Our pilot study shows that purely local enhancements remain insufficient to reliably bridge this gap. We therefore propose SpecSteer, an asymmetric collaborative inference framework that synergizes private on-device context with cloud-scale reasoning. SpecSteer casts collaboration as Bayesian knowledge fusion and repurposes speculative decoding as a distributed alignment protocol, yielding a Draft{--}Verify{--}Recover pipeline: the on-device model drafts personalized sequences; the cloud validates via a ratio-based mechanism that decouples reasoning verification from private context, filtering logical flaws without accessing raw user context; upon rejection, a steering recovery injects local intent during correction. Experiments demonstrate that SpecSteer successfully closes the reasoning gap and achieves superior personalized generation performance, while delivering a 2.36 X speedup over standard baselines."
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<abstract>Realizing personalized intelligence faces a core dilemma: sending user history to centralized large language models raises privacy concerns, while on-device small language models lack the reasoning capacity required for high-quality generation. Our pilot study shows that purely local enhancements remain insufficient to reliably bridge this gap. We therefore propose SpecSteer, an asymmetric collaborative inference framework that synergizes private on-device context with cloud-scale reasoning. SpecSteer casts collaboration as Bayesian knowledge fusion and repurposes speculative decoding as a distributed alignment protocol, yielding a Draft–Verify–Recover pipeline: the on-device model drafts personalized sequences; the cloud validates via a ratio-based mechanism that decouples reasoning verification from private context, filtering logical flaws without accessing raw user context; upon rejection, a steering recovery injects local intent during correction. Experiments demonstrate that SpecSteer successfully closes the reasoning gap and achieves superior personalized generation performance, while delivering a 2.36 X speedup over standard baselines.</abstract>
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%0 Conference Proceedings
%T SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation
%A Lv, Hang
%A Liang, Sheng
%A Wang, Hao
%A Zhang, Yongyue
%A Gu, Hongchao
%A Guo, Wei
%A Lian, Defu
%A Liu, Yong
%A Chen, Enhong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lv-etal-2026-specsteer
%X Realizing personalized intelligence faces a core dilemma: sending user history to centralized large language models raises privacy concerns, while on-device small language models lack the reasoning capacity required for high-quality generation. Our pilot study shows that purely local enhancements remain insufficient to reliably bridge this gap. We therefore propose SpecSteer, an asymmetric collaborative inference framework that synergizes private on-device context with cloud-scale reasoning. SpecSteer casts collaboration as Bayesian knowledge fusion and repurposes speculative decoding as a distributed alignment protocol, yielding a Draft–Verify–Recover pipeline: the on-device model drafts personalized sequences; the cloud validates via a ratio-based mechanism that decouples reasoning verification from private context, filtering logical flaws without accessing raw user context; upon rejection, a steering recovery injects local intent during correction. Experiments demonstrate that SpecSteer successfully closes the reasoning gap and achieves superior personalized generation performance, while delivering a 2.36 X speedup over standard baselines.
%U https://aclanthology.org/2026.emnlp-main.47/
%P 1029-1046
Markdown (Informal)
[SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation](https://aclanthology.org/2026.emnlp-main.47/) (Lv et al., EMNLP 2026)
ACL
- Hang Lv, Sheng Liang, Hao Wang, Yongyue Zhang, Hongchao Gu, Wei Guo, Defu Lian, Yong Liu, and Enhong Chen. 2026. SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 1029–1046, Budapest, Hungary. Association for Computational Linguistics.